The local optimization approach is a common approach applied for determining chemical shift values in computational chemistry to moderate the outsized computational costs of ab initio methods. It typically involves optimizing a single structure to find the (local) minimum on the potential energy surface, with a tacit assumption that such minimum is close to the global minimum and that its weight in the ensemble of finite-temperature structures is very high. However, in complex systems like zeolite MFI, identifying the global minimum is particularly challenging, and even if successful, its importance in the finite-temperature structure ensemble may be low as many low-energy local minima are present as well. To assess the inherent variance of this simplified method, we locally optimized 20,000 structures generated from a molecular dynamics simulation of an MFI system with 17 water molecules per unit cell, containing a single aluminium atom substituted for silicon in the T5 position. All optimizations were performed using neural network potentials (NNP). While this does not strictly test the quality of the single-structure method (which would require comparing the prediction of the putative global minimum against the MD average and experimental data), it provides insights into the variation present across a set of local minima. For each locally optimized structure the chemical shifts were calculated using the KRR method. The goal was to assess whether this extensive application of the local optimization method could yield stable and reproducible chemical shift values or if the MD based approach, which averages the chemical shift values over the (un-optimized) snapshots from the trajectory, provided a more reliable result.