Flow-through organosolv fractionation is a promising route for lignin-first biorefineries, allowing selective lignin extraction under comparatively mild conditions. However, rational reactor- and packing-design is hindered by a limited mechanistic understanding of how flow heterogeneity and particle morphology influence mass transfer and breakthrough behavior. This work presents a computationally model that bridges experimental flow-through organosolv extractions with predictive computational fluid dynamics. Lignin removal is modelled as a predominantly mass-transfer-limited process, combining free-flow Fickian transport with convection at particle surfaces and an intraparticle diffusion step. Kinetic resistance is incorporated into a compact, time-dependent bulk term calibrated against experimental outlet signals, yielding a lightweight formulation suitable for design exploration. Model is validated against experimental data demonstrating that the dominant discrepancies arise from system start-up timing rather than deficiencies in the underlying physics. After correcting for this lag, the model accurately reproduces extraction magnitudes and trends. With the core model established, the influence of particle geometry was thoroughly investigated. By maintaining equivalent effective surface area and pore content, mixed-shape particle assemblies were shown to best replicate validated behavior. Elliptical particles produced broader, lower extraction peaks due to reduced specific perimeter and fewer corner-induced high-shear regions, while spherical particles yielded earlier but diminished peaks associated with more uniform film transfer and lower specific area. These results highlight trade-offs between surface renewal, residence time distribution, and peak lignin concentration. Despite limitations related to idealized geometries, fixed particle spacing, and ultraviolet–visible spectrophotometry (UV–Vis) calibration that conflates lignin quantity and quality, the model allows robust virtual experiments for evaluating temperature, flow rate, packing strategy, and reactor internals. The approach provides a practical tool for guiding scale-up and for integrating future extensions such as mass-loss-driven shape evolution, contact packing, and spatially resolved transport calibration.