Additionally, we have shown the power of such large-scale CSP to train transferable machine learned potentials for organic solid-state systems. A committee neural network potential trained on single-point periodic DFT lattice energies achieved excellent accuracy in correcting our force field energy landscapes to the DFT level, reducing energy errors by approximately 8 fold. While the NNP performance is reduced on molecules reserved as an extrapolation set compared to those seen in training, the potential still demonstrates improvements to the quality of the resulting CSP rankings increasing the number of experimental structures ranked as the global energy minimum by 18% overall. We further demonstrated the development of a transferable MACE potential using structures derived from the CSP landscapes to allow reoptimisation of crystal structures, testing it successfully on those molecules in our set where the molecular geometry distortion between the conformation in the known crystal structure and the gas-phase-optimised one was largest. The results showed improved structural agreement with experimental structures in almost all cases and much improved energy rankings, moving several poorly-ranked observed structures to the global energy minimum.