An overview of the workflow described here is shown in Figure . The fundamental idea consists of NNPs trained by active learning using query-by-committee (QbC) techniques to identify high uncertainty structures in CSP landscapes and Monte Carlo trajectories. The CSP landscapes can be explicitly calculated for this purpose or re-used from prior studies. In this work, only the oxalic acid landscape was calculated for the purpose of training NNPs. The more computationally demanding landscapes for resorcinol and TTBI were taken from earlier works. All landscapes were originally generated by a quasi-random sampling of the crystal packing space using our Global Lattice Energy Explorer (GLEE) code. The initial trial structures were generated from rigid molecules and lattice energy minimized using the FIT exp-6 potential combined with atom-centred multipole electrostatics (FIT+DMA). In the case of resorcinol, to account for the conformational flexibility, crystal structures were generated using a pool of rigid conformations and, following rigid-molecule lattice energy minimization, were fully relaxed at the dispersioncorrected DFTB level (DFTB3-D3). Further details are provided in the supporting information.