Furthermore, we have illustrated how these potentials can be readily extended to points on the lattice energy surface far from the initial CSP structures through on-the-fly training within Monte Carlo simulations. The resulting potential yield stable crystal structure optimization. Future studies will investigate training separate models for the intramolecular and intermolecular components towards an improved description of conformationally flexible systems, applying transfer learning and multi-fidelity approaches to reach higher levels of theory efficiently, and a more advanced training scheme to reduce variability in the active learning. Our results here further exemplify the potential of MLPs to accelerate organic molecular CSP, and with the improvement in MLP models and descriptors ongoing there is still much more to be realized. UK's HEC Materials Chemistry Consortium, which is funded by EPSRC (EP/R029431), this work used the Young HPC facility.