We present a statistical machine learning approach to predict high quality, hybrid functional (PBE0) DFT energies for crystal structure prediction by relating crystal structure to lattice energy, trained on a subset of predicted crystal structures through descriptors of local atomic environments. The main development that we present is a multi-fidelity model for energetic predictions. The recursive GPR modelling approach takes advantage of correlations between lattice energies calculated using different methods to increase predictive accuracy using low numbers of the highest level, most computationally demanding calculation. Thus, accurate predictions of the high-level calculated energies can be obtained at a fraction of the cost of the full calculations by making use of less computationally demanding, lower-fidelity data. The method is applied to the crystal structure landscapes of three small molecules that have proven challenging for force field-based prediction: oxalic acid, maleic hydrazide and urazole.