In this work, we address one of the central challenges in translational computational hemodynamics: how to effectively individualize cardiovascular models in scenarios where patient data are scarce and the parameter space is large. We propose an approach to construct patient-specific 1D blood flow models from patient data; specifically, the methodology relies on calibrating model parameters using a limited set of flow rate and pressure measurements. As a model to describe the systemic circulation, we adopt a simplified version of the Anatomically Detailed Arterial Network model which is combined with a Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES) to find the best set of parameters that minimize the discrepancy metric between model prediction and available measurements. The calibration problem is addressed in two steps. In the first one, the influence of each physical parameter on the personalization process is quantified via an exhaustive global sensitivity analysis. Then, CMA-ES is employed to estimate the optimal model parameters. The findings of this study demonstrate the feasibility of deriving accurate patient-specific models in a setting characterized by small data and a high-dimensional parameter space. Furthermore, we provide a comprehensive analysis of the regional impact of physical parameters on the waveform characterization and their potential impact on clinically relevant biomarkers.