The diversity of structures typically found on organic CSP landscapes provides a great resource for training MLPs that are relevant across a wide scope of the lattice energy surface. The results presented here demonstrate how active learning combined with ∆-learning provides an efficient workflow to generate MLPs from these datasets. The resulting potentials can be applied directly to correct the energies of CSP structures to higher levels of theory or can be extended by on-the-fly training within MC simulations to accurately describe the energy surface beyond the local energy minima.